Computer based system for pricing an index-offset deposit product

ABSTRACT

A computer-based method for determining a value of an index-offset deposit product, having a principal amount, a term, a specified guaranteed amount, and an index credit comprising the step of setting trial values for fixed-income-linked crediting parameters for the product implying an expected fixed-income-linked credit component at the end of the term. The method further comprises the steps of determining a cost for an option paying an index-linked credit component such that a composite index credit together with the principal is at least equal to a specified guaranteed amount.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of Ser. No. 13/160,012 filed Jun. 14,2011, which is a continuation in part of application Ser. No. 12/538,488filed Aug. 10, 2009, which is a continuation of application Ser. No.10/463,180 filed Jun., 16 2003 and now issued as U.S. Pat. No.7,590,581, all of which are herein incorporated entirely by reference.

BACKGROUND

1. Field of the Invention

The present invention relates generally to financial products, morespecifically to computer-based systems for pricing financial products,and, even more particularly, to a computer-based system for pricing anindex-offset deposit product.

2. Background of the Invention

A call option is a financial instrument that gives its holder the right(but not the obligation) to purchase a given security at a pre-specifiedprice, called the strike price or exercise price, from the optionseller. This structure allows the option holder to profit if the priceof the security exceeds the strike price at the time of expiry of theoption. At the same time, the maximum possible loss to the holder islimited to the price paid for the option if the security is worth lessthan the exercise price, since the holder is not forced to buy thesecurity at an above-market price.

Options usually have a limited lifespan (the term) and have two mainstyles of exercise, American and European. In an American-exercise calloption, the security may be purchased for its strike price at any timeduring the term. In a European-exercise call option, in contrast, thesecurity may only be purchased at the end of the term.

An indexed call option is one in which the role of “securities price” isplayed by an index such as the S&P 500 or the Nasdaq 100. Sincedelivering the basket of securities that comprise the index is usuallyimpractical, indexed call options are usually cash-settled. This meansthat if the index is greater than the strike price at time of exercise,the option seller pays the option holder the difference in price incash: if the index is less than or equal to the strike, no payment ismade.

Many investors currently purchase indexed call options directly to helpachieve a desired balance of risk and return in their investmentportfolios. Many investors and consumers also benefit indirectly frominvestments in such options when they buy index-linked deposit productssuch as indexed annuities or indexed certificates of deposit (CD's).This is because index-linked deposit products are usually constructedfrom a mixture of indexed call options and fixed-income instruments suchas bonds or mortgages.

Investors and consumers obtain valuable benefits through the use ofindex-linked deposit products currently available in the market, suchas:

-   -   a. The ability to benefit from increases in the index while        protecting principal; and    -   b. Achievement of diversification by linking investment returns        to an index aggregating the performance of multiple issuers,        rather than just one.

There are also some disadvantages associated with currently availableindex-linked products, including:

-   -   a. The lack of fixed-income linkage, i.e., the inability to take        advantage of increases in interest rates after product purchase,        because returns are tied to one index for the length of the        term; and,    -   b. Lower-than-desired “participation rates” (the proportion of        increases in the index credited to the product), especially        during times of low interest rates or high index volatility.

The last point may require explanation. Participation rates are low wheninterest rates are low because most of the amount deposited must beinvested in fixed income to guarantee return of principal, leavinglittle left over to buy indexed options. Similarly, higher indexvolatility leads to higher option prices for the most common types ofoptions, driving participation rates down.

The investor or consumer therefore must face the situation thatachievement of index participation and a guarantee of principalgenerally precludes earning an attractive interest rate. A difficultchoice must be made.

References useful in understanding the present invention include:

-   An Introduction to the Mathematics of Financial Derivatives,    Salih N. Neftci (2001)-   Financial Calculus, Martin Baxter and Andrew Rennie (1996)-   Martingale Methods in Financial Modelling, MarekMusiela and    MarekRutkowski (1997)-   Changes of Numeraire, Changes of Probability Measure and Option    Pricing, Geman, H., El Karoui, N. and Rochet, J. C. (1995)-   Arbitrage Theory in Continuous Time, Tomas Bjork (1998)-   Beyond average intelligence, Michael Curran, Risk 5 (10), (1992)-   The complete guide to option pricing formulas, Espen Gaarder Haug,    1997-   Measuring and Testing the Impact of News on Volatility, Robert F.    Engle & Victor K. Ng (1993)-   Option Pricing in ARCH-Type Models, Jan Kallsen & Murad S. Taqqu    (1994)-   The GARCH Option Pricing Model, Jin-Chuan Duan (1995)-   Pricing Options Under Generalised GARCH and Stochastic Volatility    Processes, Peter Ritchken & Rob Trevor (1997)-   An Analytical Approximation for the GARCH option pricing model by    Jin-Chuan Duan, Geneviève Gauthier, and Jean-Guy Simonato (2001)-   The Market Model of Interest Rate Dynamics, Alan Brace, Dariusz    Gatarek, and Marek Musiela (1997)-   A Simulation Algorithm Based on Measure Relationships in the    Lognormal Market Models, Alan Brace, MarekMusiela, and Erik Schlogl    (1998)-   LIBOR and swap market models and measures, Farshid Jamshidian (1997)-   Interest Rate Models Theory and Practice, Damiano Brigo & Fabio    Mercurio (2001)-   Drift Approximations in a Forward-Rate-Based LIBOR Market    Model, C. J. Hunter, P. Jäckel, and M. S. Joshi (2001)-   The Market Price of Credit Risk: An Empirical Analysis of Interest    Rate Swap Spreads by Jun Liu, Francis A. Longstaff, and Ravit E.    Mandell (2000)-   Modern Pricing of Interest-Rate Derivatives, Riccardo Rebonato    (2002)-   An Empirical Comparison of GARCH Option Pricing Models, K. C. Hsieh,    Peter Ritchken (2000)-   Modern Portfolio Theory and Investment Analysis (4^(th) ed.),    Edwin J. Elton and Martin J. Gruber (1991)-   The Art of Computer Programming, Vol. 2, Donald E. Knuth,    Addison-Wesley (1969)-   The Art of Computer Programming, Vol. 3, Donald E. Knuth,    Addison-Wesley (1973)-   Algorithms, Robert Sedgewick (1983)-   Handbook of Mathematical Functions (AMS55), Milton Abramowitz and    Irene A. Stegun (1972)-   Matrix Computations, Gene H. Golub and Charles F. Van Loan (1989)-   Numerical Methods, Germund Dahlquist and Ake Bjorck, Prentice-Hall    (1974)-   Algorithms for Minimization without Derivatives, R. P. Brent,    Prentice-Hall (1973)-   Numerical Recipes in C, William H. Press, William T. Vetterling,    Saul A. Teukolsky, Brian P. Flannery, Cambridge University Press,    1992-   Numerical Solution of Stochastic Differential Equations Peter E.    Kloeden and Eckhard Platen, (1995)-   Stochastic Simulation, Brian D. Ripley, Wiley (1987)-   Intel Architecture Optimization Reference Manual, Intel (1998)-   Inner Loops by Rick Booth (1997)-   The Software Optimization Cookbook, Richard Gerber, Intel Press    (2002)-   Principles of Compiler Design by Alfred V. Aho and Jeffrey D. Ullman    (1977)-   File Structures: An Analytic Approach, Betty Joan Salzberg (1988)-   A Very Fast Shift-Register Sequence Random Number Generator, Scott    Kirkpatrick and Erich P. Stoll, Journal of Computational Physics    40, (1981) 517-526-   Monte Carlo Simulations: Hidden Errors from “Good” Random Number    Generators, A. M. Ferrenberg, Y. J. Wong, and D. P. Landau (1992)-   The Ziggurat Method for Generating Random Variables, George    Marsaglia and Wai Wan Tsang (2000)-   Remark on Algorithm 659: Implementing Sobol's quasirandom sequence    generator, Stephen Joe and Frances Y. Kuo, ACM Transactions on    Mathematical Software, March 2003-   A comparison of three methods for selecting values of input    variables in the analysis of output from a computer code, M. D.    McKay, R. J. Beckman, and W. J. Conover, Technometrics, 21    (2):239-245, (1979)-   Elements of Sampling Theory, Vic Barnett (1974)-   Singular Value Decomposition and Least-Squares Solutions, G. H.    Golub and C. Reinsch, in J. H. Wilkinson and C. Reinsch (editors),    Handbook for automatic computation, vol. II: “Linear Algebra”,    Springer Verlag (1974)

Accordingly, there is a need for an indexed deposit product structurepermitting the purchaser to enjoy an attractive combination of aplurality of index-linkages and at least one fixed-income-linkage whileguaranteeing a specified percentage of principal. The plurality ofindex-linkages may take the form of a combination or a blended index oftwo or more indices. There is correspondingly a need for acomputer-based system for pricing such an indexed deposit productstructure.

SUMMARY OF THE INVENTION

In one aspect, the present invention comprises a computer-based methodfor determining a value of an index-offset deposit product, having aprincipal amount P, a term T, a specified guaranteed amount G, and anindex credit C. The method comprises the step of setting trial valuesfor fixed-income-linked crediting parameters for the product implying anexpected fixed-income-linked credit component F at the end of the term Tand determining a cost for an index option paying aindex-linked creditcomponent E equal to the sum of one or more index-linked creditsubcomponents E_(i), E=ΣE_(i), such that the index credit C=E+F, to bepaid at T, together with the principal P, is at least equal to G. Themethod further comprises the step of summing the index option cost,present value of principal, and present value of fixed-income-linkedcredit component to determine said value of said index-offset depositproduct.

These as well as other advantages of various aspects of the presentinvention will become apparent to those of ordinary skill in the art byreading the following detailed description, with appropriate referenceto the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The nature and mode of operation of the present invention will now bemore fully described in the following detailed description of theinvention taken with the accompanying drawing figures, in which:

FIG. 1 is a screen shot which shows how the program can be used tocalculate the rate sensitivities of a traditional rate annuity;

FIG. 2 is a screen shot which shows how the program can be used tocalculate the price and interest rate sensitivities of a product withfixed income linked index credits with the equity index allocation stillat zero;

FIG. 3 is a screen shot which shows how the program can be used tocalculate the price and interest rate sensitivities of a product with aconstant base rate and with an equity index allocation of 50%;

FIG. 4 is a screen shot which shows how the program can be used tocalculate the price and interest rate sensitivities of a product with atreasury linked base rate and with an equity index allocation of 50%;

FIG. 5 is a screen shot similar to that of FIG. 4 but showing that anequity participation rate of 75% has been introduced;

FIG. 6 is a screen print illustrating the method of operation of thefind_cpp operation; and,

FIG. 7 is a screen print illustrating the method of operation of thecpp_patc operation.

DETAILED DESCRIPTION General Description

A brief description of an index-offset deposit product is one thatprovides the purchaser with a notional allocation of principal toindex-linked and fixed-income-linked allocations and two guarantees:

-   -   a guarantee that a specified percentage (often 100%) of        principal will be paid to the holder at the end of a specified        term, and    -   a guarantee that the composite index credit computed from the        index-linked components and fixed-income-linked index credit        components at the end of the term will be non-negative, i.e.,        that positive and negative index credit components from the        index-linked notional allocation and the at least one        fixed-income-linked notional allocations can offset each other        so long as the composite index credit itself is nonnegative.

In one arrangement, the index credit component for the index-linkednotional allocation may be based on a single published index such as theS&P 500 index or NASDAQ index. Alternatively, the index credit componentfor the index-linked notional allocation may be derived from a blend ora combination of more than one index. As just one example, the indexcredit component may be derived in part from the S&P 500 index incombination with at least one other index such as the NASDAQ index orthe Barclays Capital Aggregate Bond Index (formerly referred to as theLehman Aggregate Bond Index). The indices may be weighted, for example,so that S&P 500 returns have a weight of 60% and Barclays CapitalAggregate Bond Index returns have a weight of 40%. As those of skill inthe art will recognize, other index blending and/or combinationarrangements may also be used.

The index credit component for the fixed-income-linked notionalallocation may be based on a Treasury-based or Libor-based interest rate(external index) or may be based on rates declared by the issuer(internal index). We refer to a Constant Maturity Treasury rate andzero-coupon bond yields below for the fixed-income-linked notionalallocation for the sake of concreteness, but the extension to differentexternal and internal interest indices is straightforward.

Having defined a generic index-offset deposit product, we can definespecific index-offset deposit products, such as deferred annuities, lifeinsurance, certificates of deposit, and bonds, as specializations of thegeneric product. Index credits for such products are calculated fromfixed income and indexed notional allocations and index credit componentparameters, with a guarantee that a specified percentage of principalwill be paid at the end of the term.

The index-offset deposit product has some features in common withindexed deposit structures that have previously been described in theliterature, (see, e.g., U.S. Pat. No. 6,343,272, a system for managingequity-indexed life and annuity policies). However, there are importantdifferences between the present invention and prior products, which leadto the present invention being a more efficient product, i.e., providinga more attractive combination of index-linkage and fixed-income linkageunder the constraint that a specified percentage of principal must beguaranteed. The key differences are:

-   -   a. The notional index-linked and fixed-income-linked allocation        of principal, and    -   b. The guarantee that the index credit itself, although not each        index credit component or subcomponent separately, will be        nonnegative.

These differences make the product more difficult to price than existingindexed products, especially in the general case in which thefixed-income-linked credit component is indexed to a Treasury orLibor-based rate, because the interest-rate and index risks interact.Key interactions include:

-   -   a. the dependence of arbitrage-free pricing for index options on        realized short-term risk-free interest rates, so that index        exposures vary depending on the shape, level, and volatility of        the yield curve, and    -   b. the fact that the index credit at the end of the term, and        hence interest rate exposures, depend on the expected index        credit component from the index-linked notional allocation,        because of the potential for offset between the index-linked and        fixed-income-linked index credit components.

Pricing a product with such interactions requires the development ofsoftware specifically designed to take these interacting risks intoaccount.

Detailed Description of Product Mechanics

The principal paid for the index-offset deposit product is notionallyallocated to the index-linked allocation and the fixed-income-linkedallocation. The notional allocation percentages are determined by theissuer, not the purchaser, and might be (for example) 50% each. Forexample, the notional allocation percentage of 50% for the index-linkedallocation may be split among two or more indices (with returns weighted50%/50% or 60%/40%, for example) so as to create a blended index.

In one exemplary arrangement, index-linked and fixed-income-linkedcredit components are determined over each term and the index credit iscalculated at the end of the term. The length of the term might be 5 to7 years for a typical product. Alternatively, in one exemplaryarrangement, a first index-linked and a first fixed-income-linked creditcomponents are determined over a first period of time (e.g., a term) anda second index-linked credit component may be determined over a secondperiod of time. In one preferred arrangement, the index credit may thenbe calculated at the end of both the first term and the second term.

The fixed-income-linked credit component may be determined bycompounding together the “base rates” for each year of the term. In thiscompounding, the base rate is taken to be at least as large as aspecified floor rate (e.g., 2%, varying by year of the term), but nolarger than a specified cap rate (e.g., 8%, once again varying by yearof the term).

The base rate for the first year of the term is declared by the issuer(e.g., 2%). At successive intervals during the term (e.g., annually),the base rate changes by a percentage of the change in a benchmarkyield, such as the 5-Year Constant Maturity Treasury rate, or the yieldon a 5-year zero-coupon bond. Different percentages (participationrates) may apply to increases and decreases, and the percentages may bepositive or negative.

The first indexed credit subcomponent may be based on a percentage(e.g., 100%) of the (signed) increase in the first index over the term,measured from the starting point to the end value, or to an averagevalue such as the weekly average of the first index over the lastquarter (3 months) of the term. Such an index may be the Nasdaq 500.Similarly, a second indexed credit subcomponent may be based on apercentage (e.g., 100%) of the (signed) increase in the index over theterm, measured from the starting point to the end value, or to anaverage value such as the weekly average of the index over the lastquarter (3 months) of the term. Such a second index may comprise theRussell 2000 or the EuroStoxx 50. A similar analysis may be undertakenif a third or more indexed credit subcomponent is used.

The interaction between the plurality of index credit components is thata decrease in one index can offset an increase in the other index and/oran increase in the fixed-income-linked credit component. However, aspecified percentage of principal is guaranteed (e.g., the overall orcomposite index credit of the two or more credit components itselfcannot be negative). For example, assume that 50% of the deposit isnotionally allocated to a first fixed-income-linked credit component and50% is notionally allocated to an index-linked credit componentcomprising two or more indices, i.e. having two or more index-linkedcredit subcomponents.

Risk Management Considerations

Risk management for deposit products usually requires attention to theindex exposures created by the product (for index-linked products) andto the interest-rate exposures created by the product (forfixed-income-linked and index-linked products). Risk managementconsiderations for Applicants' proposed systems and methods are morecomplicated than for currently available products in at least threeways:

Sensitivity to Forward Interest Rates

Deposit-taking institutions such as banks and insurance companies havetypically managed their assets and liabilities to try to minimize thedifference between asset duration and convexity and liability durationand convexity. A good discussion of duration and convexity for depositproducts is found in The Management of Bond Portfolios (Chapter 19 ofModern Portfolio Theory and Investment Analysis, by Edwin J. Elton andMartin J. Gruber).

Use of duration and convexity for insurance carriers, for example, is sowidely accepted that it has been formalized in regulations such as NewYork Regulation 127, which uses Macaulay duration as the criterion fordetermining how well the assets and liabilities of a carrier arematched.

Duration and convexity measures, which assume parallel shifts in theyield curve, are not very useful in managing a more generalfixed-income-linked product like the current invention. Theinterest-rate exposure created by indexing to, for example, a ConstantMaturity Treasury, is considerably different than the exposure arisingfrom guaranteeing a fixed interest rate.

Measuring the sensitivity of the market value of the liability tochanges in individual forward rates is a more generally usefulmethodology than measuring duration and convexity. The following example(for a five-year term) shows the difference in sensitivity to forwardinterest rates for a fixed-income-linked product with no index linkage:

Forward Rate GIC Fixed-Income Linked 1 −0.98 −0.98 2 −0.98 −0.77 3 −0.97−0.55 4 −0.96 −0.37 5 −0.95 −0.21 6 0.00 0.75 7 0.00 0.55 8 0.00 0.33 90.00 0.16 10  0.00 0.00 Total −4.84 −1.09

The “Total” row shows duration, as traditionally measured. For atraditional deposit product, like a Guaranteed Investment Contract (GIC)the total is useful information, showing essentially the duration of azero-coupon bond broken out by the forward rates. The total in theFixed-Income Linked exposures (which assume 100% linkage to upward movesin the 5-Year CMT rate and 50% linkage to downward moves) is not veryuseful, as it is a sum of positive and negative components that are notlevel by forward period. Backing a five-year GIC with a five-yearzero-coupon bond would achieve a good asset/liability match, but backingthe above fixed-income-linked product with a 1.1 year bond would be veryrisky if the yield curve were to steepen.

Forward Rate/Index Interaction

Interest-rate exposures for options are usually captured by a measurecalled rho, which assumes (like duration and convexity) that yield curveshifts are parallel. For this product, these interest rate exposuresmust be broken out by individual forward rates in the same way as in theprevious item, to allow them to be managed properly under the assumptionthat yield curve shifts need not be parallel.

Index-Linked/Fixed-Income-Linked Interaction

The simple example above does not take into account the interactionbetween the index-linked credits and the fixed-income-linked credits.Depending on the current and expected levels of the index, the amount tobe credited will vary, and so its present value (and hence the interestrate exposures of the product) will also vary.

If the fixed-income-linked credits are based on an external index thenthe current yield curve and interest volatilities will also affect theexpected amount of interest to be credited to the policy, which in turnaffects index exposures because of the offset between thefixed-income-linked credit component and the index credit component orcomponents.

Simple Pricing Example

Pricing for this product is done at the time of issue and encompassessetting the index-linked and fixed-income-linked notional allocations ofprincipal, and then setting the crediting component parameters for eachof the notional allocations. For the index-linked notional allocation,this involves setting a participation rate (which will often be set at100% for ease of marketing and explaining the product) for the firstindex and second (or more) index (if used). For the fixed-income-linkednotional allocation this involves setting the base rate, the upward anddownward participation rates, and the cap and floor rates.

One strategy to hedge the return guaranteed to the purchaser is to buyan in-the-money indexed call option with some of the amount depositedand a 7-year zero-coupon bond with the rest. The problem is how much toinvest in each of these investments to hedge the return properly. Weapproach this by examining the index credit component for thefixed-income-linked notional allocation, first under the assumption thatit is offered in isolation from the index-linked notional allocation,and then allowing for the interaction between the index creditcomponents.

Stand-Alone Fixed-Income-Linked Crediting Rate Example

The notional fixed-income-linked allocation may be 50% of principal.Since the overall product guarantee is 100% of principal, the “annualexcess guarantee cost” is 0%. If more than 100% of principal wereguaranteed at the end of the term, this “annual excess guarantee cost”would be greater than zero.

The issuer could therefore afford to credit the following on thenotional fixed-income-linked allocation, if it were stand-alone ratherthan combined with the index-linked notional allocation, assuming anEarned Rate of 5.61% and an expense factor of 1.37%:

-   -   a. Earned Rate−expense factor−annual excess guarantee cost; or    -   b. 5.61%−1.37%−0%;    -   c. or 4.24% at the end of each year.

Discounted at 5.61% (the earned rate), the present value of 4.24% at theend of each year is 0.240009427.

Combined Fixed-Income-Linked Crediting Rate

We can solve (by bisection, regula falsi, Brent's method, or otherroot-finding method), that a rate of 4.828% can be credited on thefixed-income-linked notional allocation, i.e., almost 60 bp higher thanthe stand-alone case.

The end-of-term guarantee is 100% of principal. If the value of theindex-linked notional allocation falls from 50% of principal to 30.45%of principal at the end of the term, then the index credit will be zero,and the product's value will be precisely that of the principalguarantee. If the value of the index-linked notional allocation endsabove 30.45% of principal, then there will be a positive index credit.So the issuer could buy a call option that pays off if the value of theindex-linked notional allocation at the end of the term is at leastequal to 0.3045/0.5000 of its original value. This is equivalent to astrike price of 60.9% of the initial index value.

Now with a strike price of 60.9% of the initial index value and theabove assumptions, the cost of the 7-year call option is 47.986% of theamount being hedged.

Since the index-linked notional allocation is 50% of the deposit, thecost of the index portion is 47.986% times 0.50, or 23.993% of thedeposit.

Since the actual cost (23.993%) equals the available cost (24%) withinrounding limits, this confirms that 4.828% is the right interest rate.

Alternate Combined Fixed-Income-Linked Crediting Rate

Another way to see that 4.828% is the right interest rate is to assume alower rate (say 4.5%) and try to work through the logic above.

At the end of the term, the value of the fixed-income-linked notionalallocation will grow to 50%×1.045⁷, or 68.04%, of the deposit.

The end-of-term guarantee is 100% of principal. If the value of theindex-linked notional allocation falls to 31.96% of the deposit by theend of the term, then the end-of-term guarantee will control, and therewill be no index credit. If the value of the index-linked notionalallocation ends above 31.96% of principal, then there will be a positiveindex credit. The issuer therefore must buy a call option that pays offif the value of the index-linked notional allocation is at least equalto 0.3196/0.5000 of its original value. This is equivalent to a strikeprice of 63.92% of the initial index value.

Now with a strike price of 63.92% of the initial index value, and theabove assumptions, the cost of the 7-year call option is 46.266% of theamount being hedged.

Since the index-linked notional allocation is 50% of the deposit, thecost of hedging the index-linked notional allocation is 46.266% times50% of the deposit, or 23.133% of the deposit. This is less than the24.0% we have available to spend.

Therefore, the base rate for the fixed-income-linked notional allocationcan exceed 4.5%.

Financial Models Required for Pricing

The example above shows how to price the product assuming constantinterest rates and a given starting value for index volatility. This isa valid method for producing a quick approximate price and is thereforeuseful in its own right. However, a slower but more accurate method forpricing the product is also useful because a) index volatility is notconstant but instead changes stochastically, b) interest rates vary byterm to maturity (this variation by term is usually referred to as the“yield curve”), and c) interest rates are not constant but varystochastically over time. These issues become particularly acute whenthe fixed-income-linked index component is tied to an external indexrate, but they are important issues for the product, as described above,regardless of the exact product configuration.

A good introduction to current approaches to financial modeling ofequity and interest-rate derivatives is An Introduction to theMathematics of Financial Derivatives by Salih N. Neftci.

Index-Offset Deposit Product—Pricing Method

We price using the NA-GARCH (Nonlinear Asymmetric GeneralizedAutoregressive Conditional Heteroscedasticity) option model, allowingfor stochastic index paths and stochastic index volatility, and theLibor Market Model, allowing for an arbitrary initial yield curve andstochastic interest rates. Note that the well-known Black-Scholes optionpricing model can be obtained as a special case of NA-GARCH in whichvolatility is constant.

The fully-stochastic method for pricing the index-offset deposit producthas the following steps:

-   -   a. Generate a set of yield curve scenarios consistent with        valuation parameters;    -   b. Generate an index scenario for each yield curve scenario,        consistent with the valuation parameters and the yield curve        scenario;    -   c. Apply the index crediting parameters to determine a terminal        account value for the product for each scenario;    -   d. Apply a market discount factor to the terminal account value        for each scenario to produce a discounted terminal account value        for each scenario; and    -   e. Compute the average of the discounted terminal account        values.

A description of the key equations of the NA-GARCH Model and LiborMarket Model follows.

The NA-GARCH Model Model Domain: Indices and Option Prices KeyCharacteristics of the Model:

1. The model has risk-neutral and physical settings.2. Index volatility is stochastic and incorporates skew.3. Market declines are generally associated with increases involatility.4. Implied volatilities tend to be a little higher than physicalvolatilities.5. Model allows arbitrage-free hedging and pricing of options andfutures.6. A discrete time, not SDE (stochastic differential equation), model.

Outline of Mathematical Formulation:

1. Index movements and changes in instantaneous volatility are driven bythe same normal random variate.2. Parameters control asymmetry (tendency of volatility to increase asmarket drops) and long-term mean volatility.

Key Equations:

ln(S _(t+1) /S _(t))=(r _(f) −d)+λh _(t) ^(1/2)−½h _(t) +h _(t)^(1/2)υ_(t+1)

h _(t+1)=β₀+β₁ h _(t)+β₂ h _(t)(υ_(t+1) −c)²

What the Variables Mean:

S_(t+1) and S_(t) are the values of the index at successive intervals,r_(f) is the risk-free yield over an interval,d is the dividend yield on the securities comprising the index over thesame interval,λ is a risk parameter (zero for arbitrage-free pricing),h_(t) is the instantaneous variance (volatility squared) over theinterval,υ^(t+1) is a normal random variate,β₀, β₁, and β₂ are parameters controlling the level and volatility ofvolatility, andc is a parameter controlling asymmetry (i.e. the degree to which marketdeclines are associated with increases in volatility).Note: To change from the physical to the risk-neutral setting, setc:=c+λ, then set λ:=0.

Implementation Notes:

1. Applicants' proposed implementation is mostly Monte Carlo with anumber of pre-computations to achieve acceptable speed: analyticalapproximations are not very useful for this model. Although latticemethods could be used they become difficult to apply for path-dependentoptions.2. Parameters can be estimated given an option price, index, interestrate, and dividend history.

The Libor Market Model Model Domain: Yield Curves and Interest RateOptions. Key Characteristics of the Model:

1. The model has arbitrage-free and physical settings, depending onwhether the market price of risk is set to zero (arbitrage-free) or not(physical).2. In the arbitrage-free setting, the model can reproduce market pricesof bonds and fixed income options.3. In the physical setting, the model can generate realistic (i.e.simulated historical) bond price scenarios.4. The yield curve can undergo a variety of realistic non-parallelshifts.5. The correlation structure of changes in the yield curve can be basedon physical volatility data (historical time series) or current marketvolatility data (e.g. futures option prices).

Outline of Mathematical Formulation:

1. The yield curve can be modeled using different measures (e.g. forwardmeasure, spot Libor measure).2. We give the forward measure equations since the Hunter-Jäckel-Joshipredictor-corrector method is useful in pricing interest-indexedproducts.3. Bond prices divided by the numeraire are martingales.4. Forward Libor interest rates are assumed to be lognormallydistributed.5. Discrete tenors (zero-coupon bonds maturing integral periods of timefrom the initial date) are assumed. A quarterly tenor can be used forscenario generation and an annual tenor for interest-indexed productpricing.6. Natural cubic spline interpolation is used to derive bond prices atother maturities in the scenario generator.

Key Equations (Forward Measure):

L _(n)(t)=(1/δ)[B(t,T _(n))/B(t,T _(n+1))−1]

dL _(n−1)(t)=L _(n−1)(t)γ_(n−1)(t)·dW _(n)(t)

dW _(n+1)(t)=dW _(n)(t)+δγ_(n)(t)L _(n)(t)/(1+δL _(n)(t))dt

What the Variables Mean:

B(t,T_(n)) is the price at time t of a bond maturing at time T_(n),δ is the common spacing between T₀, T₁, . . . T_(n),L_(n)(t) is a forward Libor rate at time t,dW_(n)(t) is an increment in d-dimensional Brownian motion at time t,γ_(n−1) is a d-dimensional vector volatility function, and· is the inner product of two d-dimensional vectors.

Despite the name “Libor Market Model”, there is no bar to applying themodel to Treasury rates.

NA-GARCH Blending of Indices—Detailed Method of Operation

This section describes the operation of computer programs which carryout certain of the steps of the presently claimed invention. Anexemplary program is provided in the Appendix.

As described above, the index-linked credit component may be calculatedas the sum of one or more index-linked credit subcomponents. Eachsubcomponent tracks the movement of an index, e.g. the NASDAQ index orthe Barclays Capital Aggregate Bond Index. Because the subcomponents areadditive, we can implement the index-linked credit calculation byblending the indices associated with each subcomponent together(normalizing starting values of the indices to one and weightingaccording to the desired weight of the return of each index in theblended index) to create a customized or blended index and thenperforming the rest of the steps of the invention.

The following calling structure chart identifies certain key functionsof a program that can be used to estimate parameters for a blended indexusing the described NA-GARCH model. A copy of this program is reproducedin this application as Appendix A and provided below.

In the following calling structure chart, if y is indented under x thenx calls y.

testEst makeUnits jd getCsvFile getFile lowercase vecFromLine diffeveval negarch_backout3 msd

Method of Operation of Each Function in the Calling Structure Chart

testEst—The testEst function is a top-level function in the APLworkspace. The method of operation is to read in a unit (or an index)values, apply weights selected by the user to create the desired blendedindex, and then use a differential evolution algorithm to find a set ofestimates for the NA-GARCH parameters that best fit the data for theblended index. These parameter estimates are input to the computer-basedsystem for pricing an index-offset deposit product described in moredetail in U.S. Pat. No. 7,590,581, which is herein incorporated entirelyby reference.

makeUnits—Given a set of CSV (comma separated value) files in adirectory, each with its own possibly-unique set of observation dates,the makeUnits function assembles a matrix of unit (or index) values in amatrix with each row representing a specific observation date. This datarepresentation makes it easy to apply weights to create a blended indexsample using matrix/vector multiplication.

jd—The jd function finds a Julian date (as used in astronomy, forexample) for a Gregorian date in YYYMMDD format and is useful forconverting successive calendar dates into successive offsets in anarray.

getCsvFile—The getCsvFile function reads a file of comma-separatedvalues and returns a matrix of values. The method of operation is toread in an entire text file, break it into lines, parse each line oftext into numeric or character values, and then assemble the resultmatrix from the parsed lines.

getFile—The getFile function uses the APL system function {quad}nread toread a text file.

lowercase—The lowercase function converts a mixed-case vector ofcharacters to all lower-case using APL vectoring indexing.

vecFromLine—This vecFromLine function parses a line into a mixedcharacter/numeric vector and returns the vector.

diffev—The diffev function uses a modified differential evolutionalgorithm to find NA-GARCH parameters maximizing the value of the evalfunction, subject to the constraint that the parameter set be “good,” asdefined in the description of the eval function.

The method of operation of the diffev function is to start with a set ofrandom parameter estimates (a “generation”). In one preferredarrangement, this generation may comprise 128 parameter sets. Thefollowing operations are then applied successively:

-   -   a. Randomly select whether to jitter (perturb all parameters) of        to dither (perturb some parameters) the current generation when        creating candidates for the next generation.    -   b. Randomly decide whether a clipped Cauchy distribution or an        exponential distribution will be used to perturb the current        generation.    -   c. Generate new, perturbed parameter estimates for a        randomly-selected subset of the current generation, where the        probability of any member of the current generation being        selected is determined by the crossover ration.    -   d. For each parameter set in the next generation with a higher        fitness and satisfying the “goodness” constraint, replace the        parameter estimate in the current generation with the new,        “fitter” parameter estimate.    -   The diffev function is considered to have converged when both a)        the standard deviation of the function values for the current        generation is smaller than a prescribed threshold and b) at        least ten iterations have taken place. The latter condition is        intended to ensure that at least some of the parameter space has        been searched before reporting convergence.

Eval—Given an assumed set of NA-GARCH parameters, the eval functioninvokes the nagarch_backout3 function to back out a set of randomvariates (normal under the assumption that the NA-GARCH model applies tothe data) and the standard deviation of those variates. It then returnsthe negative of the sum of the squares of the random variates (so thatmaximizing the returned value will be equivalent to minimizing the sumof the squares) and a “goodness” indicator (depending on whether thestandard deviation of period returns under the assumed parameters isless than or equal to the observed standard deviation of the blendedindex data series).

The goodness indicator is necessary because unconstrained optimizationmay lead to a set of fitted parameters that minimize the sum of squares,but that do not provide a feasible parameter estimate because they implyan unrealistically high initial index volatility. In a set ofexperiments the volatility implied by parameters obtained from anunconstrained fit was at least 20 times the volatility actually used togenerate that data to be fitted.

Nagarch_backout3—Starting with an assumed NA-GARCH parameter set(including an assumed starting volatility level) and an observed dataset, this function uses the NA-GARCH recursion equation to “back-out”(i.e., solve for) random variates corresponding to the successive datapoints. This can be done because in the NA-GARCH model, if the initialvolatility is known, then the random variates driving the series can bebacked out from successive changes in the index value—supplementalinformation on the series of volatility is not required after the firststep.

The function is vectorized, i.e, one data series and maybe 128 sets ofalternate NA-GARCH parameters, the function returns 128 sets ofbacked-out random variates, each of which, under the assumption that theNA-GARCH model fits the data, is a sample from a normal distributionwith a zero mean and standard deviation of one.

Msd—the msd function finds the mean and the standard deviation of a setof numbers.

As described above, the NA-GARCH model for evolution of the indexassumes that the following equation holds:

ln(S _(t+1) /S _(t))=(r _(f) −d)+λh _(t) ^(1/2)−½h _(t) +h _(t)^(1/2)υ_(t+1)

This equation can clearly be transformed to the equivalent

S _(t+1) =S _(t)*exp(r _(f) −d)*exp(λh _(t) ^(1/2)−½h _(t) +h _(t)^(1/2)υ_(t+1)),

in which the interest rate term and noise terms have been separated. Byinduction we can write:

S _(T) =S ₀*exp((r−d)*T)*Πexp(λh _(t) ^(1/2)−½h _(t) +h _(t)^(1/2)υ_(t+1)), or

S _(T) =S ₀*exp(r*T)*exp(−d*T)*Πexp(λh _(t) ^(1/2)−½h _(t) +h _(t)^(1/2)υ_(t+1))

where r and d are now continuously compounded rates, T is the terminaldate, and all the random variation is contained in the product (II)terms. The following exp(r*T) can be referred to as an “accumulationfactor” as described below.

To calculate an option price, a discount factor must be used tocalculate the discounted expectation of the excess of the terminal indexS_(T) over the strike price. In the constant interest rate case thediscount factor is just the reciprocal of the accumulation factor, i.e.exp(−r*T), but as described below there it is sometimes useful to allowthem to be different.

We refer to the ability to separate the accumulation factor from therandom variation term as the “factoring property” of NA-GARCH.Practically it has at least three very important implications for theindex-offset deposit product pricing program:

1) For use in Monte Carlo simulations in which interest rates vary, oneset of NA-GARCH index scenarios can be precalculated assuming aninterest rate of zero. This set can then be adjusted to be consistentwith any desired interest rate simply by multiplying by the correctexp(r*T) term;2) Similarly, for use in Monte Carlo simulations in which computation ofsensitivities to changes in interest rates is desired, for instance inselecting and testing the appropriate fixed-income investment strategyfor the product, one set of NA-GARCH index scenarios can beprecalculated assuming an interest rate of zero. This set can then beadjusted to be consistent with any set of perturbed yield curves bymaking a multiplicative adjustment; and3) It is possible to apply different accumulation and discount factorswithout recomputing the index paths, and this is crucial in theapplication of forward measure models, as described next.

Terminal measure, which is defined as forward measure in which thenumeraire is the longest-term bond in the model, is convenient fordiscounting in this case. In a forward measure model the discount factorfor a European option is always the price of a zero-coupon bond maturingwhen the option expires, allowing considerable simplification. It doesnot follow from this that the accumulation factor is just the reciprocalof the zero-coupon bond price, however: although the integrated shortrate is not used directly for discounting, the Libor market model analogto the integrated short rate (the spot Libor process) is used togenerate the index paths.

For example, with a forward measure predictor-corrector model, for aproduct with a five-year term and annual indexing to the 5-yearzero-coupon Treasury, only five annual steps must be taken to get to theend of the product term with a set of simulated Treasury-indexed indexcredit components. The longest bond required to complete the simulationis one with a ten-year maturity at the time of product issue (i.e.reducing to five-year by product maturity).

This leaves the problem of how to generate a consistent set of indexedindex credit components. Approximate index scenario values at the end ofthe product term can be computed by multiplying a) an approximateaccumulation factor equal to the reciprocal of the 5-year zero-couponbond price at issue by b) a set of NA-GARCH index scenario valuescomputed with an interest rate of zero, i.e. with the interest ratedependence factored out.

This approach only provides an approximation to truly arbitrage-freeindex scenarios in the stochastic interest rate case, however. This iseasy to see because in the limiting case if we worked in the five yearforward measure, index scenario paths would be driven by only one sourceof noise (NA-GARCH index volatility) in the forward-measure model, whilethey would be driven by two (interest rate accumulation factor andNA-GARCH index volatility) in the spot Libor measure. This suggests thatusing the spot Libor process to generate the index paths (with the spotLibor process generated using the forward measure) would give the exactresult.

In fact, Theorem 2 of Geman, El Karoui, and Rochet gives the price of aEuropean option call option at time 0 (C(0)) with strike K expiring attime T as:

C(0)=B(0,T)E ^(T)[(S(T)−K)⁺]

where we have modified the notation slightly from the original paper forclarity. The price today of a zero-coupon bond maturing T years from nowis B(0,T), and E^(T) denotes expectation under the T-forward measure,i.e. using the zero-coupon bond maturing at time T as the numeraire.Since the expectation of a function of S(T) is to be taken under theT-forward measure, the terminal index itself S(T) must be generatedunder the same measure. This is most easily done in a simulation modelby generating the terminal values of the spot Libor process under theT-forward measure.

We can extend this reasoning to the index-offset deposit product, whichhas both index-linked and fixed-income-linked index credit components,although it is more convenient to perform the simulation in a differentnumeraire based on the sum of the length of the product term and theterm of the longest bond to which credits are indexed. For example, fora five-year product indexed to a five-year bond, and to an index aswell, it is convenient to perform the simulation using the ten-year bondas numeraire. The spot Libor process is simulated only to year five,since the behavior of the index after the end of the product term isirrelevant.

The plurality of index credit components (i.e., one or morefixed-income-linked components and one or more index-linked components)can then be calculated and combined in accordance with the product'screditing formula to determine the scenario-specific nonnegative indexcredit at the end of the term. Following Musiela & Rutkowski's equation(13.36), this scenario-specific index credit is accumulated to theterminal date (in this example ten years from product issue) using thethen-current 5-year zero-coupon bond price. The discounted value of theindex credit for the scenario can then be found by multiplying thisaccumulated amount by the ten-year zero coupon bond price at issue.

The price of the index credit is then determined by averaging thesediscounted values over a number of scenarios. We typically run 50,000scenarios in the Delphi implementation, but the precise number willdepend on the accuracy required and the run-time available.

Index Offset Deposit Product Pricing Programs—Detailed Description andMethod of Operation

Different implementations have been provided to enable different productconfigurations and to show how to handle both constant andstochastically varying interest rates, how different integration methodscan be used, how averaging of ending values of the index can beincorporated as well as a point-to-point (European) payoff structure,and to show how different types of externally indexed interest rates,such as Constant Maturity Treasury rates and zero-coupon bond yields canbe incorporated into the product.

Method of Operation

The program may be run as a command line in the APL interpreter. Theprogram is written to run under the APL+Win interpreter marketed byAPL2000 (www.apl2000.com) which has a full-screen windowing facility toallow for interactive editing of programs and display of output.

Exemplary embodiments of the present invention have been described.Those skilled in the art will understand, however, that changes andmodifications may be made to these arrangements without departing fromthe true scope and spirit of the present invention, which is defined bythe claims.

We claim:
 1. A computer-based method for determining a value of anindex-offset deposit product, having a principal amount P, a term T, aspecified guaranteed amount G, and an index credit C, comprising: a)setting trial values for fixed-income-linked crediting parameters forsaid product implying an expected fixed-income-linked credit component Fat the end of the term T; b) determining a cost for an option paying anindex-linked credit component Eequal to the sum of one or moreindex-linked credit subcomponents E_(i), E=ΣE_(i), such that the indexcredit C=E+F, to be paid at T, together with the principal P, is atleast equal to G; and c) summing said option cost, present value ofprincipal, and present value of fixed-income-linked credit component todetermine said value of said index-offset deposit product.
 2. Thecomputer based method of claim 1 wherein the index-offset depositproduct comprises an indexed annuity.
 3. An article of manufacturecomprising an index-offset deposit product with a specified term andspecified principal amount p and specified guaranteed amount goperatively arranged notional allocation amounts p0 and p1 at specifiedintervals such that p0>=0, p1>=0, and p0+p1=p, while guaranteeing anonnegative index credit over the term and a return of the guaranteedamount g at the end of the term, where an index credit component for thenotional amount p0 is based upon an arbitrary but specified nonzerointerest rate, and an index credit component for the notional amount p1is based on changes in a blend of more than one index.
 4. The article ofclaim 3 wherein the blend of more than one index may be modified atspecified intervals within the term.
 5. The article of claim 3 whereinthe specified guaranteed amount g is operatively arranged to allow anissuer to choose arranged notional allocation amounts p0 and p1 atspecified intervals within the term.
 6. A computer readable mediumprovided with instructions stored thereon that, in response to executionby a computing device, causes said computing device to performoperations for determining a value of an index-offset deposit product,said operations include determining a first cost for a firstindex-linked credit component; determining a second cost for a secondindex-linked credit component; determining a third cost for afixed-income-linked credit component; such that a total index credit tobe paid comprising the sum of said first credit component, said secondcredit component and said third credit component is at least equal to aspecific guaranteed amount.
 7. The method of claim 6 further comprisingthe step of summing said first cost, said second cost, said third cost,and a present value of principal to determine said value of saidindex-offset deposit product.
 8. The method of claim 6 furthercomprising the step of determining the first cost for the firstindex-linked credit component at the same time as determining the secondcost for the second index-linked credit component.
 9. The method ofclaim 6 wherein the first index-linked credit component comprises ablend of more than one index.
 10. The method of claim 6 wherein theindex-offset deposit product comprises an indexed annuity.
 11. Acomputer-based system for determining a value of an index-offset depositproduct, comprising the steps of: a) determining a cost for at least oneblended index-linked credit component and b) determining a cost for atleast one fixed-income linked credit component such that the total indexcredit to be paid comprising the sum of all the credit components is atleast equal to a specific guaranteed amount; and c) summing saidindex-linked credit component costs, said fixed-income-linked creditcomponent cost, and a present value of principal to determine said valueof said index-offset deposit product.
 12. The computer-based system ofclaim 11 wherein said index-offset deposit product comprises an indexedannuity.
 13. The computer-based system of claim 11 wherein said blendedindex-linked credit component comprises a plurality of index-linkedcredit components.
 14. A computer-based system for determining a valueof an index-offset deposit product, comprising the steps of: a)determining a cost for an index-linked credit component comprising a sumof a blend of index-linked credit subcomponents and b) determining acost for a fixed-income linked credit component such that the totalindex credit to be paid comprising the sum of the two credit componentsis at least equal to a specific guaranteed amount; and c) summing saidindex-linked credit component cost, said fixed-income-linked creditcomponent cost, and a present value of principal to determine said valueof said index-offset deposit product.
 15. The computer-based system ofclaim 14 wherein said index-offset deposit product comprises an indexedannuity.